Update pipeline.py

pipeline.py

@@ -1,34 +1,21 @@
-import numpy as np
-from typing import Dict
-
-from transformers import  Wav2Vec2Processor, Wav2Vec2ForCTC
-from pyctcdecode import Alphabet, BeamSearchDecoderCTC
+from torchvision import transforms
+from pair_classification import PairClassificationPipeline
 
 class PreTrainedPipeline():
     def __init__(self, path):
         """
         Initialize model
         """
-        self.processor = Wav2Vec2Processor.from_pretrained(path)
-        self.model = Wav2Vec2ForCTC.from_pretrained(path)
-        vocab_list = list(self.processor.tokenizer.get_vocab().keys())
-
-        # convert ctc blank character representation
-        vocab_list[0] = ""
-
-        # replace special characters
-        vocab_list[1] = "⁇"
-        vocab_list[2] = "⁇"
-        vocab_list[3] = "⁇"
-
-        # convert space character representation
-        vocab_list[4] = " "
-
-        alphabet = Alphabet.build_alphabet(vocab_list, ctc_token_idx=0)
-
-        self.decoder = BeamSearchDecoderCTC(alphabet)
-        self.sampling_rate = 16000
-
+        model_flag = 'google/vit-base-patch16-224-in21k'
+        # self.processor = feature_extractor = ViTFeatureExtractor.from_pretrained(model_flag)
+        self.pipe = pipeline("pair-classification", model=model_flag , feature_extractor=model_flag , 
+                model_kwargs={'num_labels':len(label2id),
+                            'label2id':label2id,
+                            'id2label':id2label,
+                            'num_channels':6,
+                            'ignore_mismatched_sizes': True })
+        self.model = self.pipe.model.from_pretrained(path)
+        
 
     def __call__(self, inputs)-> Dict[str, str]:
         """
@@ -39,8 +26,6 @@ class PreTrainedPipeline():
             A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing
             the detected text from the input audio.
         """
-        input_values = self.processor(inputs, return_tensors="pt", sampling_rate=self.sampling_rate).input_values  # Batch size 1
-        logits = self.model(input_values).logits.cpu().detach().numpy()[0]
-        return {
-            "text": self.decoder.decode(logits)
-        }
+        # input_values = self.processor(inputs, return_tensors="pt", sampling_rate=self.sampling_rate).input_values  # Batch size 1
+        # logits = self.model(input_values).logits.cpu().detach().numpy()[0]
+        return self.pipe(inputs)